KR20220138327A - Apparatus, method and program for providing disease symptom and treatment informetion predicting service - Google Patents

Abstract

The present invention relates to a disease symptom and treatment information prediction service providing device, method, and program capable of providing a disease symptom, treatment, and recovery information prediction service based on the similarity of the diseases of users. The method comprises: (a) a step in which a service server collects user disease-related information from a plurality of user devices; (b) a step in which the service server calculates and stores similar indicators of each user based on the collected user disease-related information; (c) a step in which the service server receives a disease symptom and treatment information prediction service providing request from a first user device among the plurality of user devices; (d) a step in which the service server extracts a first similarity indicator of a first user corresponding to the first user device and compares the extracted first similarity indicator with second similarity indicators of second users other than the first user, respectively to calculate similarity; (f) a step in which the service server extracts disease symptom and treatment information of the second user having a high similarity based on the calculated similarity; and (g) a step in which the service server predicts disease symptom and treatment information of the first user based on the disease symptom and treatment information of the second user having a high similarity and provides the corresponding prediction information to the first user device.

Description

Apparatus, method, and program for predicting disease symptoms and treatment information

The present invention relates to an apparatus for predicting disease symptoms and treatment information, and more particularly, to an apparatus for providing a disease symptom and treatment information prediction service capable of providing a disease symptom, treatment, and recovery information prediction service based on the disease similarity of users. , methods and programs.

In the recent information age, vast amounts of information are pouring in, and through this, people are provided with a lot of convenience.

Such information is generated as many people share it, that is, it can be seen that people are being provided with convenience based on information shared by other people.

In the medical service field, the paradigm of medical care is changing to data-based.

A system that can manage individual patient's treatment history and prescription history as data has been established, making it easier to share information among medical staff.

However, this is only possible within the same medical institution in which the system is built, and there is a problem that other hospitals, pharmacies, or even the patient himself/herself cannot share medical information about treatment.

In addition, patients have the inconvenience of having to search the Internet every day in order to predict a disease based on their symptoms before visiting a medical institution. There is this.

Therefore, in the future, to collect and manage disease symptoms, treatment and recovery information for a large number of patients as data, and use the data to provide predictive information that accurately predicts the user's disease symptoms, treatment and recovery information The development of technology is required.

Korean Patent Publication No. 10-2167377 (Registration Date: October 13, 2020)

An object of the present invention for solving the above-described problems is to collect and manage disease symptoms, treatment and recovery information for a number of patients as data, and utilize the data to collect disease symptoms, treatment and recovery of the user. It is to provide an apparatus, method, and program for providing a disease symptom and treatment information prediction service capable of providing predictive information accurately predicted information.

In addition, the present invention provides an apparatus, method, and program for predicting disease symptoms and treatment information that the user can easily visually check by predicting the user's disease symptoms, treatment and recovery information and providing the prediction information with various indicators. will provide

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

In a method for providing a service for predicting disease symptoms and treatment information according to an embodiment of the present invention for solving the above-described problems, the disease symptom and treatment information prediction service providing apparatus including a service server communicatively connected to a plurality of user devices A method for providing a symptom and treatment information prediction service, comprising the steps of: (a) the service server collecting user disease-related information from the plurality of user devices; (b) the service server based on the collected user disease-related information Calculating and storing the similarity index of each user, (c) receiving, by the service server, a request for providing a disease symptom and treatment information prediction service from a first user device among the plurality of user devices, (d) the service The server extracts the first similarity index of the first user corresponding to the first user device, and compares the extracted first similarity index with the second similarity indicators of second users other than the first user to determine the degree of similarity calculating, (f) extracting, by the service server, disease symptoms and treatment information of a second user with a high degree of similarity based on the calculated degree of similarity, and (g) using the service server with the second user with a high degree of similarity. Predicting the disease symptoms and treatment information of the first user based on the user's disease symptoms and treatment information, and providing corresponding prediction information to the first user device.

In an embodiment, in the step (a), when the user device logs in, it is checked whether the service subscription is authenticated, and when the service subscription authentication of the user device is confirmed, a main screen service is provided to the user device, and the user device When the user's disease-related information is input through the main screen service, the inputted user's disease-related information is received and stored in a database.

In an embodiment, the user disease-related information may include at least one of basic information about the user, disease information, and additional information.

In an embodiment, the user's basic information includes gender, age, weight, height, blood pressure, blood sugar, and meal information, and the user's disease information includes disease name, department, disease stage, treatment period, symptoms, and pain. , treatment history, prescription history, and treatment progress information, and the user's other information is characterized in that it includes family history, residential area, occupation, hospital used, and medical staff information.

In an embodiment, the step (b) categorizes user disease-related information corresponding to each user by class based on the collected user disease-related information, and calculates an index corresponding to each classified class, , it is characterized in that the similar index of each user is calculated based on the calculated index and stored in the database.

In an embodiment, in step (b), when classifying the user disease-related information by class, the user disease-related information of each user is divided into a basic information-related first class, disease information-related second class, and additional information It is characterized in that it is classified into a related third class.

In an embodiment, in the step (b), when calculating the index corresponding to the first class, each sub for gender, age, weight, height, blood pressure, blood sugar, and meal information included in the first class The index of the first class is calculated by calculating an index, setting a weight for each sub-indicator, and summing each sub-indicator value multiplied by the set weight.

In an embodiment, in the step (b), when calculating the index corresponding to the second class, the disease name, department, disease stage, treatment period, symptom, pain, treatment history, prescription included in the second class It is characterized in that each sub-indicator is calculated for the history and treatment progress information, a weight is set for each sub-indicator, and each sub-index value multiplied by the set weight is added to calculate the index of the second class. .

In an embodiment, in the step (b), when calculating the index corresponding to the third class, sub-indices for each of the family history, residence area, occupation, hospital used, and medical staff information included in the third class calculation, setting a weight for each sub-indicator, and summing each sub-index value multiplied by the set weight to calculate the index of the third class.

In an embodiment, in the step (b), when the similarity index of each user is calculated, when the first class index, the second class index, and the third class index are calculated, the first, second, A weight is set for each of the third class indexes, the set weights are multiplied by the first, second, and third class indexes, respectively, and the first, second, and third class indexes multiplied by the weights are added to the above It is characterized in that the similarity index of each user is calculated.

In an embodiment, in the step (d), when calculating the similarity, a difference value between the first similarity index of the first user and the second similarity index of the second user is calculated based on the difference value. to calculate a degree of similarity between the first user and the second user.

In an embodiment, the step (f) determines the priority of the second users in the order of the highest similarity among the second users based on the calculated similarity, and determines the priority of the second users according to the determined priority. 2 It is characterized in that the user's disease symptoms and treatment information are extracted.

In an embodiment, the step (g) calculates the expected treatment period, the expected pain index, and the expected quality of life index of the first user based on the disease symptoms and treatment information of the second user with high similarity, It is characterized in that predictive information is generated based on the calculated expected treatment period, expected pain index, and expected quality of life index.

In an embodiment, the step (g) is characterized in that when generating the prediction information, a predicted treatment scenario showing symptoms and recovery process details of the treatment process of people who have suffered a similar disease is generated.

In an embodiment, in the step (g), when generating the prediction information, it is characterized in that at least one symptom-based department is recommended based on the patient's own symptom information and similar disease patient analysis information.

In an embodiment, in the step (g), when generating the prediction information, information on the first user's disease-related medical institutions, medical staff, and departments, and information on exercise, diet, and drugs that have an effect on improving the disease It is characterized in that it generates guide information including

Meanwhile, an apparatus for providing a disease symptom and treatment information prediction service according to an embodiment of the present invention is an apparatus for providing a disease symptom and treatment information prediction service including a service server communicatively connected to a plurality of user devices, the service server comprising: and a communication unit communicatively connected to the plurality of user devices, a storage unit for storing user disease-related information and similar indicators for each user, and a control unit for controlling the communication unit and the storage unit, wherein the control unit includes the plurality of user devices through the communication unit. When user disease-related information is collected from user devices, a similar index of each user is calculated based on the collected user disease-related information and stored in the storage unit, and the first user among the plurality of user devices through the communication unit Upon receiving a request for providing a disease symptom and treatment information prediction service from the device, a first similarity index of a first user corresponding to the first user device is extracted, and the extracted first similarity index is used as a second similarity index other than the first user. The similarity is calculated by comparing the user's second similarity indicators, respectively, and the disease symptom and treatment information of the second user having the high similarity is extracted based on the calculated similarity, and the disease symptom of the second user having the high similarity is extracted. and predicting the disease symptoms and treatment information of the first user based on the treatment information, and providing corresponding prediction information to the first user device.

A computer program providing a method for providing a service for predicting disease symptoms and treatment information according to another embodiment of the present invention for solving the above-described problems is a medium for performing any one of the methods described above in combination with a computer that is hardware. is stored in

In addition to this, another method for implementing the present invention, another system, and a computer-readable recording medium for recording a computer program for executing the method may be further provided.

As described above, according to the present invention, disease symptoms, treatment and recovery information for a plurality of patients are collected and managed as data, and prediction information that accurately predicts the user's disease symptoms, treatment and recovery information using the data is generated. can provide

In addition, according to the present invention, by predicting the user's disease symptoms, treatment and recovery information and providing the prediction information with various indicators, the user can easily confirm visually.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a schematic diagram illustrating an apparatus for providing a disease symptom and treatment information prediction service according to the present invention.
FIG. 2 is a chart showing indexes for each class of user disease-related information for calculating a user's similarity index.
3 is a diagram illustrating a visualization of an expected treatment period among prediction information of a user.
4 to 6 are diagrams for visualizing an expected pain index among prediction information of a user.
7 is a diagram illustrating a visualization of an expected quality of life index among prediction information of a user.
8 is a diagram illustrating prediction information of a user displayed on a user device.
9 to 12 are flowcharts illustrating a method of providing a disease symptom and treatment information prediction service according to the present invention.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully inform those skilled in the art of the scope of the present invention, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural, unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Therefore, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless clearly specifically defined.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Before the description, the meaning of the terms used herein will be briefly described. However, it should be noted that the description of the terms is for the purpose of helping the understanding of the present specification, and is not used in the meaning of limiting the technical spirit of the present invention unless explicitly described as limiting the present invention.

1 is a schematic diagram illustrating an apparatus for providing a disease symptom and treatment information prediction service according to the present invention.

1 , the apparatus for providing a disease symptom and treatment information prediction service according to the present invention may include a service server 100 that is communicatively connected to a plurality of user devices 200 through a network.

Here, the user device 200 is a computer, UMPC (Ultra Mobile PC), workstation, net-book, PDA (Personal Digital) in which a plurality of applications (ie, applications) desired by the user can be installed and executed. Assistants), portable computer, web tablet, wireless phone, mobile phone, smart phone, pad, smart watch, Wearable devices, e-books, portable multimedia players (PMPs), portable game machines, navigation devices, black boxes or digital cameras, other mobile communication terminals, etc. can

Accordingly, the user device 200 may install a separate program or application to receive disease symptom and treatment information prediction service. may be provided.

And, the network for communication connection between the user device 200 and the service server 100 includes both wired/wireless networks, and performs pairing or/and data transmission/reception between the user device 200 and the service server 100 . It refers to a communication network that supports various communication standards or protocols for

These wired/wireless networks include all communication networks to be supported now or in the future according to the standard, and all of one or more communication protocols for it can be supported.

Such wired / wireless networks include, for example, Universal Serial Bus (USB), Composite Video Banking Sync (CVBS), Component, S-Video (analog), Digital Visual Interface (DVI), High Definition Multimedia Interface (HDMI), Network for wired connection such as RGB and D-SUB, and communication standards and protocols for them, Bluetooth, RFID (Radio Frequency Identification), infrared communication (IrDA: Infrared Data Association), UWB (Ultra Wideband), Zigbee (ZigBee), DLNA (Digital Living Network Alliance), WLAN (Wireless LAN) (Wi-Fi), Wibro (Wireless broadband), Wimax (World Interoperability for Microwave Access), HSDPA (High Speed Downlink Packet Access), LTE/LTE -A (Long Term Evolution/LTE-Advanced) or Wi-Fi Direct may be formed by a network for wireless connection and a communication standard or protocol for the same.

In addition, the service server 100 includes a communication unit 101 communicatively connected to a plurality of user devices 200 , a storage unit 103 for storing user disease-related information and similar indicators for each user, and a communication unit 101 and storage A control unit 105 for controlling the unit 103 may be included.

Here, when user disease-related information is collected from a plurality of user devices 200 through the communication unit 101 , the control unit 105 calculates a similarity index of each user based on the collected user disease-related information and calculates a storage unit ( 103), and when receiving a request for providing a disease symptom and treatment information prediction service from a first user device among a plurality of user devices through the communication unit 101, a first similarity index of the first user corresponding to the first user device , and comparing the extracted first similarity index with second similarity indexes of second users other than the first user, respectively, to calculate a similarity, and based on the calculated similarity, the disease symptom and The treatment information may be extracted, the disease symptoms and treatment information of the first user may be predicted based on the disease symptoms and treatment information of the second user with high similarity, and the corresponding prediction information may be provided to the first user device.

Then, the control unit 105, when the user device 200 logs in, checks whether the service subscription authentication is verified, and when the service subscription authentication of the user device 200 is confirmed, provides a main screen service to the user device 200, the user When the device 200 inputs user disease-related information through the main screen service, the inputted user disease-related information may be received and stored in the storage unit 103 as a database.

Here, the user disease-related information may include at least one of basic information about the user, disease information, and additional information.

For example, the user's basic information may include gender, age, weight, height, blood pressure, blood sugar, meal information, and the like, and the user's disease information includes disease name, department, disease stage, treatment period, symptoms, pain, It may include treatment history, prescription history, treatment progress information, etc., and other information of the user may include family history, residential area, occupation, hospital used, medical staff information, etc., which is only an example, but is not limited thereto. does not

In some cases, the user's disease information may further include time series information having symptom information for each disease retention period.

Next, the controller 105 classifies the user disease-related information corresponding to each user by class based on the collected user disease-related information, calculates an index corresponding to each classified class, and based on the calculated index to calculate the similarity index of each user and store it in the storage unit 103, which is a database.

Here, when classifying the user disease-related information by class, the controller 105 classifies the user disease-related information of each user into a first class related to basic information, a second class related to disease information, and a third class related to additional information can do.

For example, the first class may include gender, age, weight, height, blood pressure, blood sugar, meal information, and the like, and the second class may include disease name, department, disease stage, treatment period, symptoms, pain, and treatment history. , prescription history, treatment progress information, and the like, and the third class may include family, residential area, occupation, hospital used, medical staff information, and the like.

Then, when calculating the index corresponding to the first class, the control unit 105 calculates sub-indices for each of sex, age, weight, height, blood pressure, blood sugar, and meal information included in the first class, and each A weight may be set for the sub-indices, and the indexes of the first class may be calculated by summing the values of the sub-indices multiplied by the set weights.

In this case, the weight may be set to each sub-index of the first class such that the sum of all weights is 1.

As such, in the present invention, when calculating the first class index, A = αa1 + βa2 + γa3 + ... (where A is the first class index, and α, β, and γ are weight constants, the sum of which is 1 a1, a2, a3, ... are sub-indices including gender, age, weight, etc.), the first class index may be calculated.

In addition, when calculating the index corresponding to the second class, the control unit 105 includes the disease name, department, disease stage, treatment period, symptom, pain, treatment history, prescription history, and treatment progress information included in the second class. For each sub-indicator, a weight is set for each sub-indicator, and sub-indices multiplied by the set weight are summed to calculate the index of the second class.

In this case, the weight may be set to each sub-index of the second class such that the sum of all weights is 1.

As such, in the present invention, when calculating the second class index, B = αb1 + βb2 + γb3 + ... (where B is the second class index, and α, β, and γ are weight constants, the sum of which is 1 b1, b2, b3, .

In addition, when calculating the index corresponding to the third class, the control unit 105 calculates sub-indices for each of the family history, residential area, occupation, hospital used, and medical staff information included in the third class, and each sub-index A weight may be set in , and an index of the third class may be calculated by summing sub-index values multiplied by the set weight.

In this case, the weight may be set to each sub-index of the third class such that the sum of all weights is 1.

As such, in the present invention, when calculating the third class index, C = αc1 + βc2 + γc3 + ... (where C is the third class index, and α, β, and γ are weight constants, the sum of which is 1 c1, c2, c3, .

Next, when calculating the similarity index for each user, the control unit 105 sets weights to the first, second, and third class indexes, respectively, when the first class index, the second class index, and the third class index are calculated. and multiplying the set weights by the first, second, and third class indexes, respectively, and summing the first, second, and third class indexes multiplied by the weights to calculate a similar index for each user.

In this case, the weight may be set to each of the first, second, and third class indicators so that the sum of all weights is 1.

As such, in the present invention, when calculating the similarity index, the similarity index = αA + βB + γC (where α, β, and γ are weight constants, the sum of which is 1, A is the first class index related to basic information, and B is A similar index may be calculated by a formula consisting of a second class index related to disease information, and C is a third class index related to additional information).

Next, when calculating the degree of similarity, the control unit 105 calculates a difference value between the first similarity index of the first user and the second similarity index of the second user, and based on the difference value, the first user and the second user The similarity between them can be calculated.

For example, when calculating the degree of similarity, the controller 105 may determine that the similarity is greater as the difference value is smaller, and determine that the degree of similarity is smaller as the difference value is greater.

Then, the controller 105 determines the priority of the second users in the order of the highest similarity among the second users based on the calculated similarity, and extracts the disease symptom and treatment information of the second user according to the determined priority. can do.

For example, the controller 105 may extract only the disease symptom and treatment information of the second user having the highest similarity according to the determined priority.

As another example, the controller 105 may extract disease symptoms and treatment information of all second users having similarity values greater than or equal to a preset basic similarity according to the determined priority.

As another example, the controller 105 may extract disease symptoms and treatment information of second users belonging to the top 10% similarity according to the determined priority.

Next, the control unit 105 calculates the expected treatment period, the expected pain index, and the expected quality of life index of the first user based on the disease symptoms and treatment information of the second user with high similarity, and calculates the calculated expected treatment period, Predictive information may be generated based on the expected pain index and the expected quality of life index.

As an example, when calculating the expected treatment period of the first user, the controller 105 may calculate it based on the period from the time the first user starts treatment at a hospital to the last outpatient day or prescription drug cure date.

In addition, when calculating the expected treatment period of the first user, the control unit 105 is fully cured from the standard diagnosis period for each disease code, the arithmetic mean period of the entire treatment period of the patients experiencing the same disease of the surgical patient, and the date of registration in the diary of the same disease of the non-surgical patient An expected treatment period of the first user may be calculated based on the period up to the date of registration.

As another example, when calculating the expected pain index of the first user, the controller 105 may calculate the pain index for each period by combining the pain intensity, pain type, pain frequency, and treatment intervention level of the first user. .

Here, the pain intensity may be divided into first to tenth stages, the pain type may include muscle pain, neuropathic pain, and arthritic pain, and the pain frequency is divided into intermittent, continuous, and paroxysmal sudden. may be, and the level of treatment intervention may be divided into doctor's notice, PM use, doctor's prescription, and observation, which is only an example, but is not limited thereto.

Also, when calculating the expected pain index of the first user, the controller 105 may calculate the expected pain index of the first user based on the motion sickness index and the average value for each period of the respondent.

As another example, the controller 105, when calculating the expected quality of life index of the first user, the first user's exercise ability index, self-management index, daily activity index, pain / discomfort index, anxiety / depression index It is possible to calculate the expected quality of life index of the first user including

Also, when calculating the expected quality of life index of the first user, the control unit 105 may calculate the expected quality of life index of the first user based on the Dongbyeon association index and the average value for each period of the respondent.

Then, when generating the prediction information related to the expected treatment period, the controller 105 may generate the prediction information to display the treatment period according to the diagnosis or doctor's opinion and the currently predicted expected treatment period in parallel form.

As such, the present invention shows the user the treatment period based on a diagnosis or doctor's opinion and the currently predicted treatment period in parallel form, thereby confirming the difference in treatment period, thereby ultimately reducing health insurance premiums and indemnity insurance premiums. grounds can be provided.

In addition, when generating the predicted pain index and the expected quality of life index related prediction information, the controller 105 may generate the prediction information to be displayed separately in the form of a radar chart into the initial treatment period, the treatment period, and the complete treatment period.

As such, the present invention can provide predicted pain information and help remove unnecessary fear or fear by showing the degree of pain that people have not experienced in the form of a predictable quantitative indicator.

In addition, the present invention can help to remove anxiety factors in life by informing which part of the quality of life is sacrificed and restored over time through the expected quality of life index.

Next, when generating the prediction information, the control unit 105 may generate a predicted treatment scenario showing symptoms of the treatment process of people who have suffered a similar disease and the contents of the recovery process.

Here, the controller 105, when generating the expected treatment scenario, standardizes them recorded by people who have suffered similar diseases for each disease based on the data registered in the diary, and simulates the main symptoms, treatment process, recovery process, and treatment period. Prospective treatment scenarios can be created to display.

In addition, when generating the prediction information, the controller 105 may recommend at least one symptom-based treatment department based on the symptom information possessed by the user and the analysis information of patients with similar diseases.

Here, when recommending a department, the controller 105 may analyze the similarity of symptoms by department based on the data classified and registered in the diary, and recommend a corresponding expected department if the symptom similarity is 90% or more.

Next, when generating the prediction information, the control unit 105 provides guide information including information about the first user's disease-related medical institutions, medical staff, and departments, and information on exercise, diet, and drugs that are effective in improving the disease. can create

As such, the present invention collects and manages disease symptoms, treatment and recovery information for a plurality of patients as data, and uses the data to provide predictive information that accurately predicts the user's disease symptoms, treatment and recovery information can do.

In addition, according to the present invention, by predicting the user's disease symptoms, treatment and recovery information and providing the prediction information with various indicators, the user can easily confirm visually.

FIG. 2 is a chart showing indexes for each class of user disease-related information for calculating a user's similarity index.

As shown in FIG. 2 , according to the present invention, user disease-related information may be collected from a plurality of user devices, and a similarity index of each user may be calculated based on the collected user disease-related information.

Here, the user disease-related information may include at least one of basic information about the user, disease information, and additional information.

For example, the user's basic information may include gender, age, weight, height, blood pressure, blood sugar, meal information, and the like, and the user's disease information includes disease name, department, disease stage, treatment period, symptoms, pain, It may include treatment history, prescription history, treatment progress information, etc., and other information of the user may include family history, residential area, occupation, hospital used, medical staff information, etc., which is only an example, but is not limited thereto. does not

In some cases, the user's disease information may further include time series information having symptom information for each disease retention period.

Next, the present invention classifies the user disease-related information corresponding to each user by class based on the collected user disease-related information, calculates an index corresponding to each classified class, and based on the calculated index, each Similar indicators of users can be calculated.

Here, in the present invention, as shown in FIG. 2 , when classifying user disease-related information by class, each user's user disease-related information is divided into a first class A related to basic information, a second class B related to disease information, and additional information related to each user. It can be classified as a third class C.

As an example, the first class A may include gender, age, weight, height, blood pressure, blood sugar, meal information, and the like, and the second class B is, disease name, department, disease stage, treatment period, symptom, pain, It may include treatment history, prescription history, treatment progress information, and the like, and the third class C may include family, residential area, occupation, hospital used, medical staff information, and the like.

In addition, the present invention calculates sub-indices for each of sex, age, weight, height, blood pressure, blood sugar, and meal information included in the first class, sets a weight for each sub-indicator, and sets each sub-indicator multiplied by the set weight. The index of the first class may be calculated by summing the sub-index values.

In addition, the present invention calculates sub-indices for each disease name, department, disease stage, treatment period, symptom, pain, treatment history, prescription history, and treatment progress information included in the second class, and assigns a weight to each sub-indicator. The second class index can be calculated by summing each sub-index value multiplied by the set weight.

In addition, the present invention calculates sub-indices for each of the family history, residential area, occupation, hospital used, and medical staff information included in the third class, sets a weight for each sub-indicator, and each sub-indicator multiplied by the set weight The index of the third class may be calculated by summing the values.

Next, in the present invention, when the first class index, the second class index, and the third class index are calculated, weights are respectively set to the first, second, and third class indexes, and the set weights are applied to the first, second, Each of the third class indexes is multiplied, and the first, second, and third class indexes multiplied by the weights are summed to calculate the similarity index of each user.

3 is a diagram illustrating a visualization of an expected treatment period among prediction information of a user.

As shown in FIG. 3 , in the present invention, a user's expected treatment period is calculated based on disease symptoms and treatment information of other users with high similarity, and visualized in a bar form to provide the user with.

Here, in the present invention, the prediction information can be provided so that the treatment period according to the diagnosis or doctor's opinion and the currently predicted expected treatment period are displayed in parallel form.

As such, in the present invention, the difference in treatment period can be confirmed by showing the treatment period (eg, 4 weeks) and the currently predicted expected treatment period (eg, 2 weeks) to the user in parallel form based on a diagnosis or doctor's opinion. Ultimately, it can provide a basis for reducing health insurance premiums and reducing actual loss insurance premiums.

In addition, in the present invention, when calculating the user's expected treatment period, it can be calculated based on the period from the time the user starts treatment at the hospital to the last outpatient date or the date of completion of the prescription drug.

In addition, in the present invention, when calculating the user's expected treatment period, the standard diagnosis period for each disease code, the arithmetic mean period of the total treatment period of the patients with the same disease experience, from the date of registration in the diary of the same disease of the non-surgical patient to the registration date of the cure date Based on the period, the user's expected treatment period may be calculated.

4 to 6 are diagrams for visualizing an expected pain index among prediction information of a user.

4 to 6, the present invention calculates the user's expected pain index based on the disease symptoms and treatment information of other users with a high degree of similarity, and visualizes it in the form of a bar and a radar chart to provide it to the user. can

Here, the present invention may calculate the pain index for each period by combining the user's pain intensity, pain type, pain frequency, and treatment intervention level.

As shown in FIG. 4 , the pain intensity may be divided into first to tenth stages and visualized in a bar form and provided to the user.

In addition, as shown in FIG. 5 , the treatment intervention level is divided into doctor notice, PM use, doctor's prescription, and observation, and may be visualized in a bar form and provided to the user.

In addition, as shown in FIG. 6 , the pain types include muscle pain, neuropathic pain, and arthritic pain, and the pain frequency is divided into intermittent, continuous, and paroxysmal sudden, which can be visualized in the form of a radar chart and provided to the user. .

In addition, the present invention can calculate the expected pain index of the user based on the motion sickness index and the average value for each period of the respondent.

Here, the present invention may generate the expected pain index information to be displayed in the form of a radar chart divided into the initial treatment period, the treatment period, and the complete treatment period.

As such, the present invention can provide predicted pain information and help remove unnecessary fear or fear by showing the degree of pain that people have not experienced in the form of a predictable quantitative indicator.

7 is a diagram illustrating a visualization of an expected quality of life index among prediction information of a user.

As shown in FIG. 7 , according to the present invention, a user's expected quality of life index is calculated based on the disease symptoms and treatment information of other users with high similarity, and visualized in the form of a radar chart to provide it to the user.

Here, the present invention may calculate the user's expected quality of life index including the user's exercise ability index, self-management index, daily activity index, pain/discomfort index, and anxiety/depression index.

In addition, the present invention can calculate the expected quality of life index of the user based on the Dongbyeon association index and the average value for each period of the respondent.

In addition, the present invention may generate the expected quality of life index information to be displayed separately in the form of a radar chart into the initial treatment period, the treatment period, and the complete treatment period.

As such, the present invention can help to remove anxiety factors in life by notifying which part of the quality of life is sacrificed and restored over time through the expected quality of life index.

8 is a diagram illustrating prediction information of a user displayed on a user device.

As shown in FIG. 8 , according to the present invention, the user's disease symptoms and treatment-related prediction information may be displayed on the display screen of the user device 200 .

Here, the user's disease symptoms and treatment-related prediction information may include an expected treatment period, an expected pain index, and an expected quality of life index.

For example, the present invention may provide prediction information to display a treatment period based on a diagnosis or doctor's opinion and a currently predicted expected treatment period in a parallel form.

As such, the present invention shows the user the treatment period based on a diagnosis or doctor's opinion and the currently predicted treatment period in parallel form, thereby confirming the difference in treatment period, thereby ultimately reducing health insurance premiums and indemnity insurance premiums. grounds can be provided.

In addition, the present invention may provide prediction information so that the prediction information related to the expected pain index and the expected quality of life index is displayed in the form of a radar chart by dividing it into an initial treatment period, a treatment period, and a complete cure period.

As such, the present invention can provide predicted pain information and help remove unnecessary fear or fear by showing the degree of pain that people have not experienced in the form of a predictable quantitative indicator.

In addition, the present invention can help to remove anxiety factors in life by informing which part of the quality of life is sacrificed and restored over time through the expected quality of life index.

Next, the present invention may provide a predicted treatment scenario showing symptoms and recovery process details of the treatment process of people who have suffered from a similar disease.

Here, in the present invention, based on the data registered in the diary, it is possible to create a predicted treatment scenario so that the main symptoms, treatment course, recovery course, and treatment period are displayed in simulation by standardizing them recorded by people who have suffered similar diseases for each disease. .

In addition, the present invention may recommend at least one symptom-based treatment department based on the symptom information possessed by the person and the analysis information of patients with similar diseases.

Here, the present invention can analyze the similarity of symptoms by department based on the data classified and registered in the diary, and recommend a corresponding expected department if the symptom similarity is 90% or more.

Next, according to the present invention, it is possible to provide guide information including information on the first user's disease-related medical institutions, medical staff, and departments, and information on exercise, diet, and drugs that are effective in improving disease.

As described above, according to the present invention, by predicting the user's disease symptoms, treatment and recovery information and providing the prediction information with various indicators, the user can easily visually check the information.

9 to 12 are flowcharts illustrating a method of providing a disease symptom and treatment information prediction service according to the present invention.

As shown in FIG. 9 , according to the present invention, user disease-related information can be collected from a plurality of user devices ( S10 ).

Here, the user disease-related information may include at least one of basic information about the user, disease information, and additional information.

Next, according to the present invention, similar indicators of each user may be calculated and stored based on the collected user disease-related information (S20).

Here, the present invention classifies the user disease-related information corresponding to each user by class based on the collected user disease-related information, calculates an index corresponding to each classified class, and based on the calculated index, each Users' similar indicators can be calculated and stored in the database.

Next, according to the present invention, a request for providing a disease symptom and treatment information prediction service may be received from a first user device among a plurality of user devices (S30).

The present invention extracts a first similarity index of a first user corresponding to the first user device, and uses the extracted first similarity index with second similarity indicators of second users other than the first user, respectively. The similarity may be calculated by comparison (S40).

Here, the present invention may calculate a difference value between the first similarity index of the first user and the second similarity index of the second user, and calculate the similarity between the first user and the second user based on the difference value. .

In this case, according to the present invention, it is determined that the similarity is large as the difference value is small, and that the similarity is small as the difference value is large.

Next, according to the present invention, disease symptoms and treatment information of the second user having a high similarity can be extracted based on the calculated similarity (S50).

Here, the present invention may determine the priority of the second users in the order of the highest similarity among the second users based on the calculated similarity, and extract the disease symptoms and treatment information of the second user according to the determined priority. have.

Next, according to the present invention, the disease symptom and treatment information of the first user may be predicted based on the disease symptom and treatment information of the second user with high similarity (S60).

Here, the present invention calculates the expected treatment period, the expected pain index, and the expected quality of life index of the first user based on the disease symptoms and treatment information of the second user with high similarity, and the calculated expected treatment period; Predictive information may be generated based on the expected pain index and the expected quality of life index.

And, according to the present invention, the prediction information may be provided to the first user device (S70).

Here, in the present invention, the prediction information can be provided so that the treatment period according to the diagnosis or doctor's opinion and the currently predicted expected treatment period are displayed in parallel form.

In addition, the present invention may provide prediction information so that the predicted pain index and the expected quality of life index related prediction information are displayed in a radar chart form by dividing the initial treatment period, the treatment period, and the complete cure period.

In addition, the present invention may provide a predicted treatment scenario showing the symptoms and recovery process of people who have suffered from a similar disease.

In addition, the present invention may provide recommendation information for recommending at least one symptom-based medical department based on the symptom information possessed by the person and analysis information of patients with similar diseases.

In addition, the present invention may provide guide information including information about the first user's disease-related medical institutions, medical staff, and departments, and information on exercise, diet, and drugs that are effective in improving disease.

Next, according to the present invention, it is checked whether the disease symptom and treatment information prediction service termination request is requested ( S80 ), and if the disease symptom and treatment information prediction service termination request is requested, the prediction service may be terminated.

As shown in FIG. 10 , the present invention calculates the expected treatment period of the first user based on the disease symptoms and treatment information of the second user with high similarity when generating the prediction information (S403), and the expected pain After calculating the index ( S405 ), and calculating the expected quality of life index ( S407 ), prediction information may be generated based on the calculated expected treatment period, expected pain index, and expected quality of life index.

Here, in the present invention, when calculating the expected treatment period of the first user, the time period from the time the first user starts treatment at the hospital to the last outpatient day or prescription drug cure date may be calculated based on the calculation.

At this time, in the present invention, when calculating the expected treatment period of the first user, the standard diagnosis period for each disease code, the arithmetic mean period of the total treatment period of the patients with the same disease in surgery, the date of registration in the diary of the same disease of the non-surgical patient, and the registration date of the cure date Based on the period up to , the expected treatment period of the first user may be calculated.

And, in the present invention, when calculating the expected pain index of the first user, the pain index for each period may be calculated by combining the pain intensity, pain type, pain frequency, and treatment intervention level of the first user.

In this case, in the present invention, when calculating the expected pain index of the first user, the expected pain index of the first user may be calculated based on the motion sickness index and the average value for each period of the respondent.

Next, in the present invention, when calculating the expected quality of life index of the first user, the first user's exercise ability index, self-management index, daily activity index, pain/discomfort index, and anxiety/depression index are included. A user's expected quality of life index may be calculated.

In this case, in the present invention, when calculating the expected quality of life index of the first user, the expected quality of life index of the first user may be calculated based on the Dongbyeon association index and the average value for each period of the respondent.

11, in the present invention, after setting the standard treatment period for each disease code based on the disease symptoms and treatment information of the second user with high similarity (S302), the expected treatment period of the first user is calculated and (S303), calculating the expected pain index (S305), calculating the expected quality of life index (S307), and generating a predicted treatment scenario based on the calculated expected treatment period, expected pain index, and expected quality of life index and (S308), a department may be recommended (S309).

Here, the expected treatment scenario may include symptoms related to the treatment process of people who have suffered from a similar disease and the contents of the recovery process.

That is, the present invention can generate a predicted treatment scenario to display the main symptoms, treatment course, recovery course, and treatment period in simulation by standardizing those recorded by people who have suffered similar diseases for each disease based on the data registered in the diary. .

In addition, the present invention may recommend at least one symptom-based treatment department based on the symptom information possessed by the person and the analysis information of patients with similar diseases.

Here, the present invention can analyze the similarity of symptoms by department based on the data classified and registered in the diary, and recommend a corresponding expected department if the symptom similarity is 90% or more.

12, as another embodiment of the present invention, in the course of treatment of all kinds of diseases, people suffering from diseases similar to themselves are treated with signs, symptoms, and time series-based treatment processes and symptom changes, hospitals and departments used. It is to search and recommend the most similar type of patient by comprehensively combining and searching indicators (parameters) such as , treatment, etc.

To this end, the service server 10 of the present invention may perform steps S101 to S109 prior to step S111 in order to utilize data based on user information about existing subscribers and disease treatment and recovery information. have.

Specifically, it classifies or defines symptoms and/or diseases based on pre-stored user information about existing subscribers and disease treatment and recovery information (S101), and defines diseases and their stages based on a combination of symptom indicators (S101) ( S103).

In addition, a search algorithm for searching other users with similar symptoms and/or diseases is defined based on a combination of multiple indicators (S105), and after selectively applying at least one of the multiple indicators (S107), The results are classified by analyzing the user information about the patient and the disease treatment and recovery information (S109).

On the other hand, in order to receive a disease treatment and recovery information sharing service, the user inputs information about at least one of name, age, gender, and residence area as basic user information using the user device 20, which is his/her terminal ( S201), input the symptom information you are experiencing (S203). You can also input more pain information along with symptom information.

Thereafter, a detailed symptom index is input through the user device 20 (S205), a time series symptom index is input (S207), and treatment background information is input (S209).

When the user transmits the information input through steps S201 to S209 using the user device 20 as user information to the service server 10 ( S111 ), the service server 10 uses the user device 20 . Considering that the user (requester) has requested the provision of the disease treatment and recovery information sharing service, operations for providing prediction information are performed.

That is, the service server 10 searches for a user having a similar symptom and/or disease among existing users based on the user information received from the requester (S113).

In this case, the search may be performed based on a preset search algorithm.

After generating prediction information about the requester based on the user information, disease treatment and recovery information of at least one user found as a result of the search in step S113, and the user information of the requester (S115), the generated prediction information is sent to the requester It is transmitted to the user device 20, which is a terminal of

As a result, the requestor confirms ( 211 ) the prediction information received from the service server 10 through the user device 20 , and the predicted disease name, predicted treatment period, predicted treatment procedure, predicted symptom, and predicted pain included in the predicted information. You can check information about at least one of them.

On the other hand, the prediction information generated in step S115 is stored in the database of the service server to update the data set, and based on the updated data set, prediction information and/or guide information for other users can be generated. make it possible

In addition, the present invention can utilize big data technology to integrate and manage collected information.

Also, the service requester may be a new subscriber or an existing subscriber.

If the service requester is a new subscriber, a procedure of entering information necessary for registering as a new subscriber or as a non-subscribing user, and performing registration according to the inputted information may be preceded.

On the other hand, the present invention, without receiving the user's user information and / or disease treatment and recovery information from the user, based on techniques such as parsing, scraping or crawling, medical institutions, government It is also possible to collect disease treatment and recovery information of individuals (patients) managed by related institutions, such as institutions.

As such, the present invention collects and manages disease symptoms, treatment and recovery information for a plurality of patients as data, and uses the data to provide predictive information that accurately predicts the user's disease symptoms, treatment and recovery information can do.

In addition, according to the present invention, by predicting the user's disease symptoms, treatment and recovery information and providing the prediction information with various indicators, the user can easily confirm visually.

The method according to an embodiment of the present invention described above may be implemented as a program (or application) to be executed in combination with a server, which is hardware, and stored in a medium.

The above-described program is C, C++, JAVA, machine language, etc. that a processor (CPU) of the computer can read through a device interface of the computer in order for the computer to read the program and execute the methods implemented as a program It may include code (Code) coded in the computer language of Such code may include functional code related to a function defining functions necessary for executing the methods, etc. can do. In addition, the code may further include additional information necessary for the processor of the computer to execute the functions or code related to memory reference for which location (address address) in the internal or external memory of the computer should be referenced. have. In addition, when the processor of the computer needs to communicate with any other computer or server located remotely in order to execute the functions, the code uses the communication module of the computer to determine how to communicate with any other computer or server remotely. It may further include a communication-related code for whether to communicate and what information or media to transmit and receive during communication.

The storage medium is not a medium that stores data for a short moment, such as a register, a cache, a memory, etc., but a medium that stores data semi-permanently and can be read by a device. Specifically, examples of the storage medium include, but are not limited to, ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage device. That is, the program may be stored in various recording media on various servers accessible by the computer or in various recording media on the computer of the user. In addition, the medium may be distributed in a computer system connected by a network, and computer-readable codes may be stored in a distributed manner.

The steps of the method or algorithm described in relation to the embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can realize that the present invention can be embodied in other specific forms without changing its technical spirit or essential features. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

100: service server
101: communication department
103: storage
105: control unit
200: user device

Claims (10)

A method of providing a disease symptom and treatment information prediction service of an apparatus for providing a disease symptom and treatment information prediction service including a service server communicatively connected to a plurality of user devices, the method comprising:
(a) collecting, by the service server, user disease-related information from the plurality of user devices;
(b) calculating, by the service server, a similarity index of each user based on the collected user disease-related information and storing the same;
(c) receiving, by the service server, a request for providing a disease symptom and treatment information prediction service from a first user device among the plurality of user devices;
(d) the service server extracts a first similarity index of a first user corresponding to the first user device, and uses the extracted first similarity index as a second similarity index of second users other than the first user calculating a similarity by comparing them with each other;
(f) extracting, by the service server, disease symptoms and treatment information of a second user having a high degree of similarity based on the calculated degree of similarity; and
(g) the service server predicts the disease symptom and treatment information of the first user based on the disease symptom and treatment information of the second user with high similarity, and transmits the corresponding prediction information to the first user device A method of providing a service for predicting disease symptoms and treatment information, characterized in that it provides. The method of claim 1,
Step (b) is,
Classifying user disease-related information corresponding to each user by class based on the collected user disease-related information, calculating an index corresponding to each classified class, and based on the calculated index, the similarity of each user A method of providing a service for predicting disease symptoms and treatment information, characterized in that calculating an index and storing it in a database. 3. The method of claim 2,
Step (b) is,
Disease, characterized in that when classifying the user disease-related information by class, classifying the user disease-related information of each user into a first class related to basic information, a second class related to disease information, and a third class related to additional information How to provide predictive services for symptom and treatment information. 4. The method of claim 3,
The first class is
Gender, age, weight, height, blood pressure, blood sugar, including meal information,
The second class is
Includes disease name, department, disease stage, treatment period, symptoms, pain, treatment history, prescription history, and treatment progress information;
The third class is
A method of providing a service for predicting disease symptoms and treatment information, characterized in that it is included in family, residential area, occupation, hospital used, and medical staff information. 4. The method of claim 3,
Step (b) is,
When calculating the similarity index of each user, when the first class index, the second class index, and the third class index are calculated, a weight is set to each of the first, second, and third class indexes, and The set weight is multiplied by the first, second, and third class indexes, respectively, and the first, second, and third class indexes multiplied by the weights are summed to calculate the similarity index of each user. How to provide predictive services for disease symptoms and treatment information. The method of claim 1,
The step (g) is,
Calculate the expected treatment period, expected pain index, and expected quality of life index of the first user based on the disease symptoms and treatment information of the second user with high similarity, and calculate the calculated expected treatment period, expected pain index, and expected A method of providing a disease symptom and treatment information prediction service, characterized in that the prediction information is generated based on the quality of life index. 7. The method of claim 6,
The step (g) is,
When generating the prediction information, a method of providing a disease symptom and treatment information prediction service, characterized in that generating a predicted treatment scenario showing symptoms and recovery process details of the treatment process of people who have suffered from a similar disease. 7. The method of claim 6,
The step (g) is,
When generating the prediction information, the disease symptom and treatment information prediction service providing method, characterized in that it recommends at least one symptom-based treatment department based on the patient's symptom information and similar disease patient analysis information. In combination with a computer that is hardware, any one of claims 1 to 8 is stored in the medium to perform the service providing method for predicting disease symptoms and treatment information. A computer program that provides a method for providing information forecasting services. In the disease symptom and treatment information prediction service providing apparatus comprising a service server communicatively connected to a plurality of user devices,
The service server,
a communication unit communicatively connected to the plurality of user devices;
a storage unit for storing user disease-related information and similar indicators for each user; and,
A control unit for controlling the communication unit and the storage unit,
The control unit is
When user disease-related information is collected from the plurality of user devices through the communication unit, a similar index of each user is calculated based on the collected user disease-related information and stored in the storage unit, and the plurality of user diseases are calculated through the communication unit. Upon receiving a request for providing a disease symptom and treatment information prediction service from a first user device among user devices, a first similarity index of a first user corresponding to the first user device is extracted, and the extracted first similarity index is used as the second user device. The similarity is calculated by comparing the second similarity indicators of second users other than the first user, respectively, and disease symptoms and treatment information of a second user having a high similarity is extracted based on the calculated similarity, and the similarity is Disease symptoms and treatment information, characterized in that predicting the disease symptoms and treatment information of the first user based on the high disease symptoms and treatment information of the second user, and providing the corresponding prediction information to the first user device Predictive Service Provisioning Device.

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